Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

Most of our lives are conducted in the cyberspace. The human notion of privacy translates
into a cyber notion of privacy on many functions that take place in the cyberspace. This …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while keeping data localized. This framework faces several systems-oriented …

We consider a scenario involving computations over a massive dataset stored distributedly
across multiple workers, which is at the core of distributed learning algorithms. We propose …

We propose a novel coding theoretic framework for mitigating stragglers in distributed
learning. We show how carefully replicating data blocks and coding across gradients can …

Codes are widely used in many engineering applications to offer robustness against noise.
In large-scale systems, there are several types of noise that can affect the performance of …

We consider the problem of massive matrix multiplication, which underlies many data
analytic applications, in a large-scale distributed system comprising a group of worker …

How can we optimally trade extra computing power to reduce the communication load in
distributed computing? We answer this question by characterizing a fundamental tradeoff …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

Distributed model training is vulnerable to byzantine system failures and adversarial
compute nodes, ie, nodes that use malicious updates to corrupt the global model stored at a …